2026-06-01T01:59:08Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/281042026-02-03T11:17:09Zcom_10630_2254col_10630_37953
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Ortiz-García, Andrés
author
Munilla-Fajardo, Jorge
author
Martínez-Murcia, Francisco Jesús
author
Górriz-Sáez, Juan Manuel
author
Ramírez-Aguilar, Francisco Javier
2023-11-22T08:07:25Z
2023-11-22T08:07:25Z
2018-10-16
Ortiz, Andrés & Munilla, Jorge & Martínez-Murcia, Francisco & Gorriz, Juan & Ramírez, Javier. (2018). Empirical Functional PCA for 3D Image Feature Extraction Through Fractal Sampling. International Journal of Neural Systems. 29. 10.1142/S0129065718500405.
https://hdl.handle.net/10630/28104
10.1142/S0129065718500405
Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is especially important in the early diagnosis of dementia. In this work, we present a technique that allows using specific time series analysis techniques with 3D images. This is achieved by sampling the image using a fractal-based method which preserves the spatial relationship among voxels. In addition, a method called Empirical functional PCA (EfPCA) is presented, which combines Empirical Mode Decomposition (EMD) with functional PCA to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis. The devised technique has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), achieving accuracies up to 93% and 92% differential diagnosis tasks (AD versus controls and PD versus Controls, respectively). The results obtained validate the method, proving that the information retrieved by our methodology is significantly linked to the diseases.
eng
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Alzheimer, Enfermedad de
Parkinson, Enfermedad de
Diagnóstico por imagen
Empirical Functional PCA for 3D Image Feature Extraction Through Fractal Sampling.
journal article
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IJNS 1850040 1st Prf.pdf